Spatial-Temporal Traffic Prediction With an Interactive Spatial-Enhanced Graph Convolutional Network Model

Accurate traffic prediction is crucial for effective traffic control and risk assessment. Traffic data exhibits a distinct nature, characterized by the interplay of swift, sudden short-term variations and enduring, extended long-term trends within specific regions. This intricate intermingling and interaction give rise to diverse spatial propagation patterns. Successful traffic prediction models necessitate mastering multi-scale temporal and dynamic spatial correlations, as well as their intricate interrelationships. In this study, we present a novel spatial-temporal traffic prediction framework named <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u>nteractive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>patial-Enhanced <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u>raph <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u>onvolution <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u>etwork (ISGCN). Our key innovation lies in the introduction of a novel dynamic graph convolution module, which not only captures overarching spatial correlations but also unveils the concealed evolution of dynamic spatial correlations over time. By seamlessly integrating the graph convolutional module with temporal sample convolution and interaction blocks, we adeptly bridge multi-scale temporal correlations with the acquired dynamic spatial correlations. Additionally, we harness diverse temporal granularities data to comprehensively capture global temporal correlations. Experiments conducted on four real-world traffic datasets illustrate that ISGCN outperforms diverse types of state-of-the-art baseline models.